Moderating effects of socioeconomic status and geographical location on the Health4Life school-based intervention

Highlights • Universal eHealth school-based multiple behaviour change RCT among adolescents.• Latent growth models assessing SES and geographical location as moderators of RCT.• Diet and diet-related intentions varied by geographical location over 24-months.• Sociodemographic factors can influence intervention efficacy.• Tailored approaches may be beneficial for tackling adolescent risk behaviour disparities.


Introduction
Sociodemographic inequalities, including socioeconomic status (SES) and geographical location (e.g., urban versus rural areas), significantly contribute to chronic disease burden and are critical determinants of access to resources and health outcomes in society (Australian Institute of Health and Welfare, 2023; Robards et al., 2018).Adolescents from low SES and geographically remote contexts are particularly susceptible to this burden (Australian Institute of Health and Welfare, 2021).The 2018 Australian Burden of Disease Study (Australian Institute of Health and Welfare, 2021), reports a progressive increase in disability-adjusted life years from affluent to disadvantaged socioeconomic areas, and from major cities to remote areas, with 1.6 and 1.4 times higher rates, respectively.Despite unique differences between adolescents of low SES and geographically remote backgrounds, both groups face similar challenges in achieving health equity.Therefore, the term "disadvantaged" is used to describe these individuals in this context.Disadvantaged adolescent populations encounter various obstacles that impede their access to health, including stigmatisation, lack of social support, limited access to affordable services, and education opportunities (Australian Institute of Health and Welfare, 2023; Robards et al., 2018).
Disadvantaged adolescent populations, both in Australia and globally, exhibit higher rates of modifiable lifestyle risk behaviours such as poor diet, alcohol use, and tobacco smoking (Australian Institute of Health and Welfare, 2020; Warren et al., 2017;Wiggins et al., 2020).
These may continue into adulthood, increasing the risk of chronic disease and associated burden, especially when they occur together (Krokstad et al., 2017).Adolescence is a critical developmental period (Sawyer et al., 2012), characterised by increased risk-taking tendencies, including experimentation with alcohol and smoking (Degenhardt et al., 2016), and consuming unhealthy foods (Australian Institute of Health and Welfare, 2018).Clustering or the co-occurrence of these behaviours in adolescence is common (Uddin et al., 2020), and linked with adverse outcomes, including obesity (Bardach et al., 2023), reduced quality of life (Hoare et al., 2019) and mental ill-health (Champion et al., 2018;Gardner et al., 2022a).Indeed, research has reported that adolescents with a history of early-life low SES often face co-occurring adverse health and educational challenges (Villadsen et al., 2023).Therefore, prioritising prevention during adolescence is crucial for promoting healthy behaviours, mitigating the risk of chronic diseases in adulthood, and addressing health disparities experienced by this vulnerable population.
Electronic health (eHealth) interventions hold promise in benefiting disadvantaged adolescents by providing accessible and low-cost resources that can increase student engagement and implementation fidelity (Champion et al., 2019).Multiple systematic reviews have demonstrated the efficacy of universal eHealth interventions in preventing and addressing poor diet (Kemp et al., 2021), tobacco smoking (Taylor et al., 2017), and/or reducing alcohol use (Kazemi et al., 2021) among adolescents.Although, a recent meta-analysis of universal school-based prevention eHealth interventions targeting multiple health risk behaviours found them ineffective in preventing alcohol consumption or smoking and reducing fat, sugar-sweetened beverages (SSB), or snack consumption (Champion et al., 2019).The review recommended focusing on skill development and social influence and competence theories for improved outcomes.Despite these mixed findings, evidence supports the effectiveness of universal eHealth interventions in preventing or delaying the onset of modifiable health risk behaviours (Newton et al., 2017;Qiu et al., 2022).However, their effectiveness for disadvantaged adolescents is not as well-known, with only one systematic review published on eHealth prevention interventions targeting poor diet, alcohol use, and tobacco smoking among disadvantaged adolescents (Egan et al., 2023).The review indicated that eHealth interventions can be effective in targeting poor diet (e.g., decreasing SSB consumption) and alcohol use (e.g., reducing binge drinking at 1-month follow-up among intervention completers) among disadvantaged adolescents.However, it also acknowledged limitations due to the scarcity of published studies on this topic, with only 15 publications assessing 14 interventions eligible for inclusion.
The Australian Health4Life initiative is an innovative eHealth schoolbased intervention targeting multiple lifestyle risk behaviours among adolescents: alcohol use, tobacco smoking, poor diet, physical inactivity, poor sleep, and sedentary recreational screen time (Teesson et al., 2020).Co-designed with young people, Health4Life incorporates personalised feedback and is grounded in social influence, social cognitive, and self-determination theories (Champion et al., 2020).Results from a cluster randomised controlled trial (RCT) of Health4Life found significant effects on reducing behavioural intentions to try alcohol and tobacco at post-intervention (O'Dean et al., Under review), improving mental health outcomes (Smout et al., 2024) and knowledge about chronic disease risk factors over 24-months (Champion et al., 2023).However, the RCT reported no significant intervention effects on modifying alcohol or tobacco use, poor diet, physical inactivity, poor sleep or screen time across the entire sample (Champion et al., 2023).Despite Health4Life's positive reception by students (74.8%) and teachers (84%), and the significant improvement in knowledge, this did not translate into behaviour change.Health4Life baseline data (n = 6639 11-14 year-olds across NSW, WA, and QLD) revealed sociodemographic disparities in diet, alcohol, and tobacco use (Champion et al., 2021).For instance, students from regional areas were more likely to use alcohol than those from major cities, and students with lower SES were more likely to use alcohol and tobacco and have poorer diets than their peers with middle to upper SES.Given these disparities we expect that Health4Life's efficacy may differ in these subgroups, potentially due to distinct challenges influencing their health behaviours differently.Tailored approaches may be needed, however, evidence is currently unclear.
Nonetheless, considering the significant influence of SES and geographical factors on health-related behaviours, it is essential to examine their impact on the efficacy of interventions such as Health4-Life.This study aims to evaluate the moderating effects of SES and geographical location on the efficacy of the Health4Life intervention in reducing alcohol and tobacco use, improving dietary intake, knowledge of chronic disease risk behaviours, behavioural intentions, and reducing psychological distress.

Participants and procedure
The current study uses baseline to 24-month follow-up data from the Health4Life study (Teesson et al., 2020), a cluster RCT in 71 secondary schools across Australia.Recruitment details have been reported previously (Champion et al., 2023).Briefly, 71 schools were block randomised (1:1) by a biostatistician independent to recruitment to either the Health4Life intervention (N=36) or active control group (usual health education; N=35).Randomisation was stratified by school location (state/region) and gender composition (coeducational, mostly female [>60 %], or mostly male [>60 %]).It was not possible to blind students, teachers, and researchers to group assignment, as is standard with school-based interventions.
The study's parental consent approaches varied based on the ethical requirements of the schools involved.While 40 schools provided an optout option, 31 required active written and oral consent (i.e., opt-in).All students provided active written consent to participate in the study.
The intervention group received Health4Life, consisting of six webbased modules delivered during health education lessons, ideally once per week.Based on social influence, social cognitive, and selfdetermination theories to prevent multiple lifestyle risk behaviours, key behaviour change techniques are integrated into 20-minute interactive cartoon storylines with characters similar in age to grade 7 students (Champion et al., 2020).These cartoons, the core component of Health4Life, impart evidence-based information about health and social consequences of poor diet, alcohol use, tobacco smoking, physical inactivity, poor sleep, and sedentary recreational screen time, while also promoting resistance skills, normative education, and autonomous motivation.Students complete short online quizzes after each module, and factsheets for teachers and students are available to reinforce the content.Health4Life is supplemented by web-based tailored feedback on adherence to national health guidelines, optional online or teacher-led activities, and a smartphone app designed to encourage selfmonitoring of behaviours and goal setting.Control schools delivered usual health education, approximately once a week.
Participants completed self-report online surveys during class at four time points: baseline (2019); immediately following the intervention (2019); 12-months after baseline (2020); 24-months after baseline (2021).To maximise retention, two participants from each school were randomly allocated a AUD$100 gift voucher for completing the surveys.
This trial was prospectively registered with the Australian New Zealand Clinical Trials Registry (ACTRN12619000431123) and adheres to CONSORT guidelines.

Sociodemographic factors
Students provided self-reported information on gender, age, SES and geographical location.SES was categorised into lower, middle and upper groups based on Family Affluence III ridit scores (Elgar et al., 2017), and geographical location was classified as metropolitan or regional based on the Australian Statistical Geography Standard Remoteness Structure (Australian Bureau of Statistics, 2021).

Primary outcomes
Diet: The Student Physical Activity and Nutrition Survey (SPANS) measured the consumption of SSBs, fruit, vegetables, and discretionary foods (Hardy et al., 2016).A composite indicator of poor diet was then calculated.Individuals were classified as having a poor diet if they reported high SSB consumption (5-6 cups/week or 1 or more cups/day of SSB) or met two or more of the following conditions: consuming fewer than two servings of fruit per day, consuming fewer than five servings of vegetables per day, or consuming more than one serving of discretionary food items per day.The cut-offs for fruit and vegetable intake were based on the Australian Dietary Guidelines (National Health and Medical Research Council, 2013), while nutritionists and health recommendations guided SSB and discretionary food variables.
Alcohol: A single item measured participants' consumption of a full standard drink by asking participants, "Have you had a full standard alcoholic drink in the past 6 months?"(0 = No, 1 = Yes).To facilitate participants' responses, they were shown a pictorial chart displaying the standard drink quantities of various types of alcoholic beverages and sizes.
Tobacco: A single item measure from the Youth Risk Behaviour Survey assessed tobacco use with participants asked, "In the past 6 months, have you tried cigarette smoking, even one or two puffs?"(0 = No, 1 = Yes) (Brener et al., 2013).

Secondary outcomes
Knowledge: A 20-item scale assessed participants' knowledge of Health4Life study content, including awareness of chronic disease risk factors, alcohol guidelines, prevalence of alcohol and tobacco use among young Australians, and the impact of six lifestyle risk behaviours on physical and mental health.Items were presented as "True", "False", "Don't Know" statements, and scores were totalled to produce an overall knowledge score.
Behavioural Intentions: Participants self-reported their intentions to participate in or modify behaviours relating to poor diet, alcohol and tobacco use.Alcohol intentions were evaluated using established measures (Newton et al., 2012), with items for tobacco and poor diet (specifically SSBs including energy drinks, soft drinks, sports drinks, or cordial) adapted from these measures.Participants rated their likelihood of trying alcohol and tobacco on a scale from 0 (very unlikely) to 4 (very likely).For SSBs, they indicated their intention to replace them with water on a scale from 0 (not at all true of me) to 3 (very true of me) on all or most days over the next three months.Responses were then converted into binary variables for subsequent analyses in this study.Scores of 0-2 for alcohol and tobacco questions indicated no intention to engage in alcohol use or tobacco smoking, while scores of 3-4 indicated an intention to do so.Regarding SSBs, responses of 0-1 were recoded as no intention to replace SSB consumption with water, while responses of 2-3 indicated an intention to make the substitution.
Psychological Distress: The Kessler 6 (K6) scale, a widely used and validated measure, assessed psychological distress among adolescents by evaluating six symptoms experienced by individuals over the past four weeks (Furukawa et al., 2003;Mewton et al., 2016).These symptoms include feeling nervous, hopeless, or restless.Participants rated each symptom on a 5-point Likert scale ranging from "none of the time" to "all of the time".Scores were totalled to create a composite score, with higher scores indicating greater psychological distress.

Statistical analysis
A post-hoc exploratory moderation analysis was chosen for the current study.Latent growth curve models (LGMs) in Mplus (v 8.4.(Muthén & Muthén, 2017)) were used to evaluate the moderating effects of SES and geographical location on primary and secondary outcomes over 24month post baseline assessments.Various LGMs were used based on the distribution of the outcome variables, including binary, continuous, or ordinal models.To determine the optimal time structure and interpretation of slope estimates for each outcome, we explored various specifications of time scores, including linear, quadratic, and freely estimated, on unconditional LGMs.We compared model fit using Akaike information criterion (AIC), Bayesian information criterion (BIC), and sample-size adjusted BIC.Further details regarding model interpretation is provided in the appendices on page 8.The analysis applied the fullinformation maximum likelihood (FIML) estimation within the LGMs, aligning with intention-to-treat principles to handle missing data.FIML uses all available information when estimating parameters, and it is recognised for its superiority over conventional methods within the context of LGMs (Schafer & Graham, 2002).To account for the effect of testing multiple outcomes, the Benjamini-Hochberg approach, also known as the false discovery rate control, was applied (Benjamini & Hochberg, 1995).Unlike more conservative methods such as the Bonferroni correction, the Benjamini-Hochberg approach offers increased power to identify true associations when testing multiple outcomes.To interpret statistically significant interactions (p < 0.05), we conducted subset analyses that investigated the main effects of intervention within each level of the moderating variables (i.e., SES or geographical location).

Ethics
The Health4Life study was approved by the University of Sydney (2018/882), the University of Queensland ( 2019000037), Curtin University (HRE2019-0083), and ethics committees in the relevant school sectors.

Descriptive statistics
A total of 6,639 students from 71 schools participated in the baseline survey, constituting the analysed sample for this study.The students had a mean age of 12.7 years (SD 0.50; range 11-14 years).In terms of gender, 49.9% (n = 3,311) identified as male, 48.3% (n = 3,204) identified as female, 0.5% (n = 30) identified as non-binary or gender fluid, 0.1% (n = 9) had a different gender identity, and 1.0% (n = 69) preferred not to disclose their gender identity.The sample included students from diverse socioeconomic backgrounds, with 15% classified as low SES, 37% as mid SES, and 48% as high SES.Furthermore, the sample represented both metropolitan and regional areas, with 89% residing in metropolitan areas and 11% in regional areas (Table 1).Retention rates and the number of students who completed follow-up surveys are reported in Appendix Fig. 1.

Conditional growth curve models
There was a significant moderating effect of geographical location on intervention effectiveness for poor diet (OR = 1.79, 95% CI = 1.32-2.43,p < 0.001) (Fig. 1).Subset analyses revealed for those who resided regionally, there was greater growth in the odds of having a poor diet in the intervention group compared to the control (OR = 1.61, 95% CI = 1.13-2.29,p = 0.008), and little evidence of a difference between groups for those residing in metropolitan cities (OR = 0.99, 95% CI = 0.86-1.14,p = 0.915) (Appendix Table 4).There was little evidence supporting a moderation effect of SES on intervention effectiveness for poor diet (OR = 0.99, 95% CI = 0.87-1.13,p = 0.856) (Table 3).In addition, geographical location significantly moderated the intervention effectiveness in promoting behavioural intentions to swap SSB for water (OR = 0.71, 95% CI = 0.56-0.89,p = 0.024 (Fig. 2)).Subset analyses revealed for those who resided in metropolitan cities, there was a significantly greater increase in the odds of intending to swap SSB for water in the intervention group compared to the control (OR = 1.13, 95% CI = 1.01-1.27,p = 0.041), and little evidence of a difference between groups for those who resided regionally (OR = 0.97, 95% CI = 0.72-1.32,p = 0.857).There was little evidence of a moderation effect of SES on the intervention effectiveness in targeting diet-related behavioural intentions at 24-months (OR = 1.03, 95% CI = 0.92-1.15,p = 0.685).

Discussion
This is the first study to examine the moderating effects of SES and geographical location on the effectiveness of the Health4Life intervention in reducing alcohol and tobacco use, improving dietary intake, knowledge of chronic disease risk behaviours and behavioural intentions, and reducing psychological distress over 24-months.Overall, the study found little evidence for a moderation effect, with the exception of diet-related outcomes by geographical location.This is consistent with the primary outcomes of the Health4Life RCT, which revealed no significant intervention effects on alcohol use, tobacco smoking and poor diet across the entire sample (Champion et al., 2023).
Importantly, Health4Life draws from social influence, social cognitive, and self-determination theories (Champion et al., 2020), which aligns with recommendations for eHealth interventions among adolescents of low SES or of geographically remote backgrounds (Egan et al., 2023).However, Health4Life was not specifically designed to address the unique experiences of disadvantaged adolescents, who often encounter structural barriers including limited access to resources, support, and economic constraints (Australian Institute of Health and Welfare, 2023;Robards et al., 2018).These barriers create a more challenging environment for adopting and maintaining positive health behaviours compared to their more advantaged peers.For instance, healthy food options are less affordable and less available in low SES and regional areas (Love et al., 2018), and there may be lower levels of social support (Gautam et al., 2023), both of which are crucial for sustaining healthy behaviours.Moreover, Health4Life's foundational principles assume a level of autonomy and resource availability that disadvantaged adolescents may not have e.g.financial constraints limiting control over health choices.While the theoretical underpinnings of Health4Life are sound, their practical application may be undermined if positive health behaviours are not modelled or encouraged (Bandura, 2004) which may be the reality in disadvantaged adolescents' environments.Together this may explain why Health4Life was limited in effectively modifying alcohol and tobacco use, knowledge, alcohol-and tobacco-related behavioural intentions, and psychological distress in this population.
The present findings suggest that, generally, outcomes were not influenced by SES and geographical location.Instead, the intervention's effects may be attributed to other underlying mechanisms.For instance,   despite the myriad of challenges linked to varying SES and regional contexts, Health4Life maintained a positive impact on adolescents' knowledge of chronic disease risk behaviours, potentially due to other factors such as personal motivation or learning styles.The absence of significant moderation effects on other outcomes, including alcohol and tobacco use, suggests that Health4Life's impact on these behaviours may vary depending on individual characteristics and contextual factors, such as family dynamics and social support that could be targeted through tailored interventions.For example, interventions may target the home environment by involving parents.Additionally, including peers in the intervention process may help create a supportive network.These refinements may benefit Health4Life's efficacy for all participants, and to further enhance Health4Life's efficacy for low SES and regional populations, addressing structural barriers and other social determinants that could influence health behaviours is essential.Given disadvantaged adolescents exhibit higher rates of these lifestyle risk behaviours than their more advantaged peers (Australian Institute of Health and Welfare, 2020; Warren et al., 2017;Wiggins et al., 2020), this tailoring may lead to more significant and equitable health improvements among these populations.This highlights the importance of co-design and tailoring interventions to disadvantaged populations (Egan et al., 2023).Emerging interventions such as Just-in-Time-Adaptation, which use real-time data to provide personalised support through digital platforms, may also be beneficial for this population as they are easily accessible, individually tailored and practical (Partridge & Redfern, 2018).Diet-related outcomes are an exception to the null findings.Health4Life improved diet-related behavioural intentions for those residing in metropolitan areas, but not regional areas.However, this did not translate into dietary behaviour change, contrasting with a separate study that improved both behavioural intentions and fruit and vegetable consumption (Kothe et al., 2012).This discrepancy challenges the Theory of Planned Behaviour, which posits that intention is a proximal determinant of behaviour (Bosnjak et al., 2020).External factors, such as peer pressure influencing dietary choices (Ragelien ė & Grønhøj, 2020), coupled with socio-cultural influences (e.g., prevalent advertising of unhealthy foods in metropolitan areas (Richmond et al., 2020;Sainsbury et al., 2017)) may have counteracted the positive intentions fostered by the intervention, making it more challenging for these adolescents to adopt healthier eating habits.Future interventions should focus on modifying behavioural intentions and strategies to mitigate these external influences.Unexpectedly, regional participants in the intervention group had greater growth in the odds of having a poor diet compared to controls.This disparity may be due to the health education provided in regional control groups placing a stronger emphasis on dietary education than Health4Life.While Health4Life, given its multiple behaviour change framework, provided an equivalent of one to two 15-minute lessons on diet, teacher-reported data revealed that most regional control schools dedicated a minimum of one to two 40-minute lessons solely to diet education, and some delivered up to six lessons.Within the Australian curriculum, students learn about making healthy and safe food choices, including food-serving recommendations from The Australian Guide to Healthy Eating and practical advice on choosing healthy options from the school canteen (ACARA Version 9.0).The control schools' focused approach, coupled with greater frequency and depth on diet education than Health4Life, may have equipped students with more knowledge and strategies for making healthier food choices.This suggests Health4Life should incorporate a more intensive dietary education component, especially in regional areas.Although it is worth noting that data were not collected on other characteristics in the control and Health4Life schools, such as policies (canteen, nutrition, other health promotion policies) and food environments, which may have affected diet outcomes.Nevertheless, as previously mentioned, Health4Life was not specifically designed for disadvantaged adolescents, and regional areas may have unique challenges that the intervention did not adequately address.For instance, control schools may have better addressed the social and environmental context, including lower affordability or availability of healthy food, to ensure health-promoting messages were relevant, practical, and accessible within the local context.To overcome this, future studies could adopt matched sampling methods to ensure the control and intervention schools do not drastically differ in the amount of health education they deliver (Chondros et al., 2021;Hemming & Taljaard, 2023).A process evaluation of Health4Life in regional schools is also needed to understand the implementation context and identify any other factors that may have contributed to this unintended effect.Ultimately, the conflicting diet-related findings observed in this study highlight the need for further research to replicate and explore potential explanations, informing improvements in future interventions targeting dietary behaviours among disadvantaged adolescents.

Strengths and limitations
This study has several strengths and limitations.Firstly, the use of the composite diet risk score provides an overall perspective on diet, however, may overlook specific dietary nuances.Although the fruit and vegetable variables align with official government guidelines (National Health and Medical Research Council, 2013), the lack of guidelines for SSB and junk food variables required input from a multidisciplinary team, including nutritionists, and following health recommendations to determine the "at-risk" cutoffs for these variables.Additionally, the item assessing diet-related intentions focused solely on intentions to swap SSB for water.Although SSB is one relevant indicator of poor diet (National Health and Medical Research Council, 2013), future studies should adopt more comprehensive measures.
The use of the Family Affluence Scale III as a proxy measure for SES, while practical, simplifies evaluation by relying on a limited number of indicators, such as overseas holidays (Torsheim et al., 2016).This approach may overlook the multidimensional aspects of SES, including income and education.Furthermore, self-reported information introduces potential bias, as respondents may interpret scale items differently.Acknowledging limitations imposed by the COVID-19 pandemic is also essential.In Australia, adolescents in high and low lockdown states experienced increased negative emotions (Meyer et al., 2023), poorer mental health, limited physical activity and increased screen time (Gardner et al., 2022b;Goldfeld et al., 2022).Similar patterns were observed overseas (Lawrance et al., 2022).Although Health4Life was implemented in 2019, the follow-up assessments coincided with the pandemic, possibly influencing participants' response to Health4Life and their ability to apply acquired knowledge and skills.
The study sample included students from independent, public, and Catholic secondary schools across three Australian states, contributing to its diversity.However, most students were born in Australia, of middle or upper SES, and residing in major cities (Champion et al., 2021), potentially limiting generalisability.Moreover, including only inner and outer regional students, with no remote schools, impacts understanding whether Health4Life had any differential effects by diverse regional groups.Finally, geographical moderation analysis results for alcohol and tobacco outcomes require cautious interpretation due to wide confidence intervals, primarily attributed to low cell counts and low prevalence of use.
Notwithstanding these limitations, a key strength of the Health4Life study is its rigorous cluster RCT design and large sample size, making it one of the most extensive school-based studies of its nature.The meticulous development and implementation of Health4Life involved close collaboration with end-users (Champion et al., 2023), ensuring the intervention's relevance and engagement among students and teachers.Moreover, Health4Life's favourable acceptability within a school setting enhances its potential for wider applicability and impact.Future work is underway to refine Health4Life's content to better address the specific needs of low SES and geographically remote adolescents.
L. Egan et al.

Conclusions
This study offers valuable insights into how SES and geographical factors interacted with the Health4Life intervention's effectiveness in targeting alcohol and tobacco use, dietary intake, knowledge, behavioural intentions, and psychological distress.Generally, outcomes were not influenced by SES and geographical location.However, the exception to this pattern is in diet-related outcomes, with varying effects on poor diet and diet-related behavioural intentions based on participants' geographical location, underscoring the importance of considering regional differences in intervention design and implementation.Codesigning and tailoring interventions is essential to address disparities in chronic disease risk behaviours among disadvantaged adolescent populations.Future research should consider the impact of SES and geographical factors in intervention design and implementation to optimise outcomes and promote health equity.

Fig. 1 .
Fig. 1.Predicted and raw proportion of poor diet among metropolitan and regional participants in the Australian Health4Life Study.Solid lines are raw values and dotted lines are predicted values.

Fig. 2 .
Fig. 2. Predicted and raw proportion of intentions to swap SSB for water among metropolitan and regional participants in the Australian Health4Life Study.Solid lines are raw values and dotted lines are predicted values.

Table 1
Baseline (2019)sample characteristics of adolescents participating in the Australian Health4Life Study.

Table 2
Summary of logistic latent growth parameters, CIs and SE investigating the effects of SES on moderating the odds of study outcomes from the Australian Health4Life Study.

Table 3
Summary of logistic latent growth parameters, CIs and SE investigating the effects of Metropolitan-Regional on moderating the odds of study outcomes from the Australian Health4Life Study.